Evaluating vulnerability to fake news in social networks

Abstract

Understanding the spread of false information in social networks has gained a lot of recent attention. In this paper, we explore the role community structures play in determining how people get exposed to fake news. Inspired by approaches in epidemiology, we propose a novel Community Health Assessment model, whose goal is to understand the vulnerability of communities to fake news spread. We define the concepts of neighbor, boundary and core nodes of a community and propose appropriate metrics to quantify the vulnerability of nodes (individuallevel) and communities (group-level) to spreading fake news. We evaluate our model on communities identified using three popular community detection algorithms for twelve real-world news spreading networks collected from Twitter. Experimental results show that the proposed metrics perform significantly better on the fake news spreading networks than on the true news, indicating that our community health assessment model is effective.

COVID-19 Misinformation and Polarization on Twitter

“…Additionally, analyses regarding cross-group followers revealed users were much more likely to be connected to others with similar viewpoints. Taken together, these findings are consistent with existing theory and past empirical work on health-related misinformation online, supporting the existence of polarization online and the tendency for networks that spread misinformation to be more dense and insular (Rath et al, 2019;Wood, 2018).…”

“…SNA can also be used to assess the modularity of networks, or the extent to which they can be broken down into smaller, highly interconnected groups (Newman, 2006). Past research indicates that misinformation and fake news spread rapidly within highly modular networks due to their high level of connectedness (Rath et al, 2019). SNA is a valuable yet underutilized methodology among health researchers using Twitter data (Sinnenberg et al, 2017), as it can provide critical information about the social context of online misinformation that is difficult to assess using other methodologies.…”

Preprint - Sending News Back Home: Analyzing the Spread of Misinformation between Vietnam and Diasporic Communities in the 2020 Election

“…Research has been undertaken to understand the entire lifecycle of misleading information. This includes its origins [78], the different forms mis/disinformation takes [129], how it spreads across networked technologies like social media [69] and within offline contexts [115], audience-centered approaches to understand the saliency or vulnerability of individuals [134] and groups [105] to misleading information, and it's behavioural and attitudinal impacts [24]. The complexity of misinformation with regards to the multiplicity of its origins and effects and the differential impact it maintains, means that studying information disorder is a moving target [51].…”

SCARLET: Explainable Attention based Graph Neural Network for Fake News spreader prediction

“…The idea was further enforced by Morris et al [8] where they claimed that people assess credibility based on trust relationships with their neighbors in a social network. Motivated by these ideas, there has been much interest in computational models for false information spreader detection using trust, which has shown promising results [12,13]. Many computational techniques to combat false information spreading have been explored over the past decade, as summarized by Sharma et al [17].…”